Inverse design of slow light devices at telecommunication band based on metamaterials using a deep learning attempt

Abstract

Slow light devices have important applications in many fields such as optical communication, optical storage, optical signal shaping and synchronization. Although a variety of high-performance slow light devices can be purchased in the market, it is still a worthwhile research topic to design slow light devices according to specific wavelengths and requirements. Here we employ machine learning method to inverse design electromagnetically induced transparency (EIT) based slow light devices in communication band using metal–dielectric hybrid metamaterials. Three characteristic points of transmittance as well as the group refractive index and bandwidth are chosen as input parameters. By replacing the complex fully connected layer with a one-dimensional convolutional neural network (1DCNN) layer to optimize the fully connected network, the proposed model can break the limitation of passive modulation and find the best slow light structure parameters. The slow light parameters could reach a bandwidth of 38.71 nm and average group refractive index of 10.51. In addition, the model can predict hybrid metamaterial structure parameters of slow light devices in the communication band from 1400 nm to 1600 nm. By combining active and passive modulation technologies, our proposed method improves the adjustment range of design parameters of slow light devices. The procedure can be potentially applied in the design of other nano optical devices.